It has recently become possible to commercially obtain satellite and aerial images of terrain of interest from a number of sources. For example, certain large farms currently use satellite images provided by Landsat, the system of land-observing satellites operated by the federal government. Landsat satellites orbit the earth at approximately 900 km., and provide images in which each pixel represents a square area of between 1 m2 and 1E6 m2 (a pixel area of 100 m2 is common for systems designed for land-use purposes). Visible, near-infrared, shortwave infrared, thermal infrared sensors deployed on such satellites can detect, among other things, the spectral reflectance, temperature, and other physical characteristics of specified terrestrial areas.
The sensors used in generating the images used for many commercial purposes are typically characterized as either “multispectral” or “hyperspectral”. Multispectral sensors collect images of a terrain or landscape and provide a handful of wide spectral bands of imagery. These bands encompass the visible, short wave infrared, and, in some cases, thermal infrared portion of the electromagnetic spectrum. Similarly, hyperspectral sensors typically provide hundreds of narrow spectral bands of hyperspectral imagery spanning the visible, near-infrared, and shortwave infrared portion of the electromagnetic spectrum. Such sensors can produce enormous amounts of information needing to be transmitted on a limited bandwidth cross-link or down-link channel. For example, the Earth Observing System (“EOS”), which is currently scheduled to begin operation by 2001, will carry a high-resolution hyperspectral imager (the “Hyperion”) capable of resolving 220 spectral bands. The Hyperion is expected to produce a maximum output data rate of at least several hundred Mbit/s.
Such high data rates not only increase the complexity of required communications infrastructure, but may also strain the capacity of ground data storage and processing facilities. It will of course be appreciated that data compression of some type would at least partially alleviate these difficulties.
Many image compression algorithms have been designed and implemented over the years in order to address the storage and transmission needs of monochromatic and multispectral imaging sensors. Most of these algorithms have focused on the spatial character of the imagery and limitations in the human visual system. Hyperspectral imagery shares certain characteristics with monochromatic and multispectral imagery, but also possesses certain other characteristics providing additional opportunities for compression of the constituent spectral data. For example, it is well recognized that hyperspectral imagery typically exhibits high levels of spectral correlation. Like other imagery, hyperspectral imagery also exhibits some level of spatial correlation, but such correlation is often less than that inherent in other types of images. The data redundancy resulting from spectral and spatial correlation has been exploited by certain algorithms to provide relatively high compression ratios with insubstantial loss of information content. Such algorithms have been based upon vector quantization, use of the discrete cosine transform followed by trellis coded modulation, spectral re-ordering and linear prediction, and spectral signature matching.
However, existing data compression algorithms may fail to consider all parameters of an image potentially relevant to optimizing compression ratios. For example, such algorithms are not known to utilize image noise statistics for the purpose of ensuring that any compression loss be related substantially to image noise rather than to scene information.